Re‐animating Gros Morne's storyless space: From natural heritage to ecological heritage
Bibliographic record
Abstract
This paper reports on ethnographic research conducted at one of Canada's Natural World Heritage sites: Gros Morne National Park. UNESCO's criteria for the identification of natural heritage sites and its descriptions of the specific qualities of listed sites are informed by a dualist ontology that sharply separates nature and culture. The result of this separation between nature and culture is the construction of natural heritage spaces that seem to exist in a vacuum from social life, abstracted from human relations, largely devoid of human presence, and thus emptied of the many stories that make them meaningful to both Indigenous and non‐Indigenous residents. In contrast, this paper/video combination describes how natures at a Canadian natural heritage site are relationally woven with the lives of their human inhabitants. The narratives we share about Gros Morne are meant to re‐animate this site in response to the World Heritage classification, calling to attention the perpetual growth and becoming of its relational environments. We make our case by utilising a short video to recount the stories, experiences, and perspectives of a few residents who have taught us about Gros Morne. We argue that in place of natural heritage we ought to consider the concept of ecological heritage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".